追踪及凝视状态下地气光对空间目标成像影响

为了分析不同探测方式下地气光辐射对空间目标成像特性的影响,利用卫星仿真工具包(satellite tool kit,STK)设计了一个以地球同步轨道(geosynchronous orbit,GEO)卫星与中轨道(medium orbit,MEO)卫星上搭载的可见光成像器为探测平台,以高椭圆轨道(highly elli...     

基于STK的空间目标可见光散射特性建模与仿真

为给天基光学观测系统的设计和性能研究提供数据支持, 建立了基于STK的天基 探测空间目标等效星等计算模型. 选用双向反射分布函数(BRDF)五参量计算模 型, 分析了空间目标面元的可见光散射特性; 根据目标几何结构尺寸和物性参 量等建立目标几何模型, 对该模型进行面元划分, 推导了空间目标在天基观测 系统入瞳处的等效星等计算公式; 结合卫星工具包(STK)提供的太阳、目标卫 星、观测卫星三者的关系数据, 建立了基于STK的易于求解的目标星等计算模 型, 对此模型进行实例仿真和分析, 并与漫反射计算模型进行比较. 结果表 明BRDF模型得到的辐照度曲线比漫反射模型更曲折且存在峰值现象, 更符合 目标表面材料的实际特性, 此外, 与STK的结合运用, 使其可更加方便快捷地获得目 标最佳观测时段及相应的等效星等.      

近地空间短波红外恒星探测信噪比分析方法

针对星敏感器在近地空间导航应用需求,开展了短波红外恒星探测信噪比分析方法研究。基于恒星目标与天空背景辐射特性,构建了恒星探测信噪比模型,并结合光学系统及图像传感器参数完成了仿真试验。结果表明,同一星等及太阳天顶角下,Ks波段下的恒星探测信噪比最大,H波段次之,J波段最小;同一太阳天顶角及波段下,星等越小,恒星探测信噪比越大;同一星等及波长下,太阳天顶角越大,即恒星与太阳之间角距越大,恒星探测信噪比越大。本文可为新一代近地空间全天时星敏感器系统的方案设计、指标论证、评估应用提供可靠的理论方法与技术支持。     

太阳系天体引力对空间引力波探测日心编队构型的影响分析

针对太极空间引力波探测任务,建立了太阳系天体引力摄动对日心编队构型影响的数学模型,利用仿真手段分析了太阳系中行星和月球、矮行星和小行星引力摄动对空间引力波探测日心编队构型的影响,提出了一种综合考虑小行星到卫星轨道距离和星等的二重筛选方法,能够快速估计小行星相对加速度的上界.分析了日心编队构型卫星初始相位角变化对太阳系天...     

基于对抗神经网络有限角度的磁层边界CT重构技术

对地球磁层进行软X射线成像探测是近年来磁层研究的前沿方向.由二维X射线成像图重构三维磁层边界的方法研究是与成像探测相关的重要研究课题.传统的计算机断层成像技术(CT)重构方法在图像数据很少时无法得到较好的重构结果,甚至无法重构三维结构.考虑到地球磁层空间分布范围方面的约束,当前卫星任务的轨道设计很难满足扫描角度的全方位...     

一种实现光学隐身的卫星构型设计

目前对地球同步轨道(GEO)空间目标的探测和识别,主要依赖光学监视系统接收其反射的太阳光线.鉴于可见光反射原理,和空间目标可见光反射特性计算模型,提出光学隐身卫星设计策略,分别对卫星平台构型、太阳能帆板、半球形遮光罩进行设计,对整星外形进行仿真分析,最后提出分布式卫星的构想.结果表明,该卫星构型光学横截面积峰值达到0.082 m~2,该构型设计具有较高的隐蔽性,不易被光学监视系统探测识别.     

天基光学同步带目标监视跟踪模式

目前的天基空间目标观测模式多利用自然交会方式,在长时间观测的基础上实现对空间目标的遍历,模式规划中未考虑地气光反照对观测效率的影响。文章基于天基平台太阳同步轨道的特点,在传统自然交会模式的基础上引入姿态协同,新规划了两种同步带目标观测模式。将地气光反照对天基空间目标观测的影响量化为相机的离轴角约束,引入模式规划,得到的观测模式即为不受地气光反照影响的观测模式,具有工程实际应用价值,然后对几种观测模式的观测效率从姿态模式、遍历时长、观测弧长等方面进行了评价。理论分析和仿真结果均表明,区域凝视在对同步带目标编目方面具有明显优势,遍历时间小于1天,姿态模式相对简单,观测弧段相对较长。     

复杂背景下红外点目标探测概率估算

红外成像系统对点目标的探测概率在实际应用中往往受到复杂背景的极大影响,而现有的计算方法并不能适用于复杂背景下的探测概率估算.首先从红外成像系统对点目标的作用距离模型出发,分析了探测概率与信噪比、作用距离的关系,推导出单一背景下红外点目标的探测概率估算方法;然后,利用红外成像系统的亮度-灰度函数,通过背景的红外图像灰度信息计算得到其红外辐射亮度;最后,引入概率统计的思想对单一背景下探测概率的估算方法进行了改进,提出了复杂背景下红外点目标探测概率的估算方法.实验结果表明:该估算方法可以有效评估实际应用中典型复杂背景对红外成像系统的点目标探测概率的影响.      

空间目标在天基光学探测中的特性分析与仿真

分析了空间目标的分布及几何特性,研究了空间目标光学特性与天基探测系统、太阳以及目标自身参数的关系,提出了空间目标光学特性仿真的思路,建立的天基空间目标光学特性分析系统可以计算并绘制相对距离、太阳相位角及目标星等变化曲线.由于很难获取准确的空间目标实际光度数据,文中提出了对仿真结果的验证方法.通过对仿真结果的分析,得出了天基空间目标光学探测的特点,为天基光学探测和识别的研究提供了参考.      

Track detection of high-velocity resident space objects in Low Earth Orbit

The world’s economy has become heavily dependent on the services provided by satellites. With the exponential increase in satellite launches, the population of defunct or inactive hardware in space has grown substantially. This is especially true in sensitive orbits such as the Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. These objects, collectively known as orbital debris, can reach speeds of up to 28 000km h^?1 in LEO. At these orbital speeds, even the smallest of objects can pose a considerable threat to operational satellites or astronauts. This makes the monitoring, and detection, of these objects of the utmost importance. This work describes the latest detection strategy used in one of Europe’s largest Space Situational Awareness (SSA) installation; the BIstatic RAdar for LEo Survey (BIRALES) space debris radar. We present a novel bottom-up approach that makes use of single-linkage clustering to identify faint radar streaks in spectrogram data. Tests on synthetic data have shown that the detection strategy presented in this study obtains a higher detection rate when it is compared against existing methods. Unlike other approaches, this detection strategy, using the Multi-beam streak detection strategy (MSDS) algorithm, was still able to recall 90% of the track information at an Signal-to-Noise Ratio (SNR) of 2dB.     

航天光学遥感器仿真系统原理方案初步设计

介绍了航天光学遥感器仿真系统技术研究目的和意义。根据初步的研究结果，探讨了航天光学遥感器系统仿真技术方案、主要研究内容、技术途径等。重点研究了目标与背景、空间环境条件（温度场、真空、重力场和辐照）、卫星平台等对遥感器成像质量的影响和仿真内容，研究了用地面试验进行验证的方法，提出了建立系统仿真的结构体系等。     

Autonomous space target tracking through state estimation techniques via ground-based passive optical telescope

The presence of operational satellites or small-body space debris is a challenge for autonomous ground-based space object observation. Although most space objects exceeding 10?cm in diameter have been cataloged, the position of each space object (based on six orbital parameters) remains important and should be updated periodically, as the Earth’s orbital perturbations cause disturbances. Modern ground-based passive optical telescopes equipped with complementary metal-oxide semiconductors have become widely used in astrometry engineering, being combined with image processing techniques for target signal enhancement. However, the detection and tracking performance of this equipment when employed with image processing techniques primarily depends on the size and brightness of the space target, which appears on the monitor screen under variable background interference conditions. A small and dim target has a highly sensitive tracking error compared to a bright target. Moreover, most image processing techniques for target signal enhancement require large computational power and memory; therefore, automatic tracking of a space target is difficult. The present work investigates autonomous space target detection and tracking to achieve high-sensitivity detection and improved tracking ability for non-Gaussian and dynamic backgrounds with a simple system mechanism and computational efficiency. We develop an improved particle filter (PF) using the ensemble Kalman filter (KF) for track-before-detect (TBD) frameworks, by modifying and optimizing the computational formula for our non-linear measurement function. We call this extended version the “ensemble Kalman PF-TBD (EnKPF-TBD).” Three sequential astronomical image datasets taken by the Asia-Pacific Ground-Based Optical Space Objects Observation System (APOSOS) telescope under different conditions are used to evaluate three proposed TBD baseline frameworks. Given an optimal random sample size, the EnKPF-TBD exhibits superior performance to PF-TBD and threshold-based unscented KF with two-dimensional peak search (2dPS). The EnKPF-TBD scheme achieves satisfactory performance for all variable background interference conditions, especially for a small and dim space target, in terms of tracking accuracy and computational efficiency.     

家庭安全监控的实时异常检测与分类算法

同时具有好的实时性和稳健性是基于视频的安全监控系统异常检测算法的难点问题.在家庭安全监控系统中同时利用像素的亮度分量和色度分量检测变化像素,解决摄像镜头由于大尺寸对象闯入引起的图像背景变化造成运动对象检测失败的问题;提出一种实时运动对象分类算法,综合利用开关灯检测、运动距离分析和皮肤探测多种方法,区分是否出现影响家庭安全的事件;提出3关键值背景维护算法,用于消除背景振动像素,减少虚假报警.实验表明,本文异常探测算法对视频监控系统异常探测和消除虚假异常报警具有较好的实时性和稳健性.      

基于统计特征空间提取和支持向量机的极暗弱小天体检测方法

小天体检测是小天体防御和预警的前提。针对小天体目标信噪比低、检测难的问题,提出了基于统计特征空间提取和支持向量机（SVM）的极暗弱小天体检测方法。区别于传统方法基于时间或空间上目标的能量和背景噪声能量的瞬时能量差别或是瞬时能量差别的累积,对目标进行检测。该方法不依赖目标能量大小,提取运动目标穿过背景时对稳定性产生的扰动来反演运动目标。将输入的图像序列转化为单像元时序信号,划分时序窗口提取统计特征,关联形成统计特征空间,利用更高维度的变化特性检测目标变化。通过SVM将暗弱小天体检测问题转化为目标与背景的二分类问题,避开了较难解决的阈值分割问题同时具有更好的泛化性能。利用真实数据与其他经典方法进行对比分析,使得分类准确率提高4.02%。该方法能够适应更低的信噪比,在极低信噪比下仍表现出稳定的检测性能。     

A novel signal processing approach for LEO space debris based on a fence-type space surveillance radar system

The increase in space debris can seriously threaten regular activities in the Low Earth Orbit (LEO) environment. Therefore, it is necessary to develop robust, efficient and reliable techniques to understand the potential motions of the LEO debris. In this paper, we propose a novel signal processing approach to detect and estimate the motions of LEO space debris that is based on a fence-type space surveillance radar system. Because of the sparse distribution of the orbiting debris through the fence in our observations, we formulate the signal detection and the motion parameter estimation as a sparse signal reconstruction problem with respect to an over-complete dictionary. Moreover, we propose a new scheme to reduce the size of the original over-complete dictionary without the loss of the important information. This new scheme is based on a careful analysis of the relations between the acceleration and the directions of arrival for the corresponding LEO space debris. Our simulation results show that the proposed approach can achieve extremely good performance in terms of the accuracy for detection and estimation. Furthermore, our simulation results demonstrate the robustness of the approach in scenarios with a low Signal-to-Noise Ratio (SNR) and the super-resolution properties. We hope our signal processing approach can stimulate further work on monitoring LEO space debris.